Uptrend Analyzer

Uptrend Analyzer Skill

Purpose

Diagnose market breadth health using Monty's Uptrend Ratio Dashboard, which tracks ~2,800 US stocks across 11 sectors. Generates a 0-100 composite score (higher = healthier) with exposure guidance.

Unlike the Market Top Detector (API-based risk scorer), this skill uses free CSV data to assess "participation breadth" - whether the market's advance is broad or narrow.

When to Use This Skill

English:

• User asks "Is the market breadth healthy?" or "How broad is the rally?"
• User wants to assess uptrend ratios across sectors
• User asks about market participation or breadth conditions
• User needs exposure guidance based on breadth analysis
• User references Monty's Uptrend Dashboard or uptrend ratios

Japanese:

• 「市場のブレドスは健全？」「上昇の裾野は広い？」
• セクター別のアップトレンド比率を確認したい
• 相場参加率・ブレドス状況を診断したい
• ブレドス分析に基づくエクスポージャーガイダンスが欲しい
• Montyのアップトレンドダッシュボードについて質問

Prerequisites

• Python 3.9+ with the requests library (CSV parsing uses the stdlib csv/io modules)
• Internet connection to fetch CSV data from GitHub (no API key required)
• No paid API subscriptions needed

Difference from Market Top Detector

| Aspect | Uptrend Analyzer | Market Top Detector |

|--------|-----------------|-------------------|

| Score Direction | Higher = healthier | Higher = riskier |

| Data Source | Free GitHub CSV | FMP API (paid) |

| Focus | Breadth participation | Top formation risk |

| API Key | Not required | Required (FMP) |

| Methodology | Monty Uptrend Ratios | O'Neil/Minervini/Monty |

Execution Workflow

Phase 1: Execute Python Script

Run the analysis script (no API key needed):

python3 skills/uptrend-analyzer/scripts/uptrend_analyzer.py

The script will:

• Download CSV data from Monty's GitHub repository
• Calculate 5 component scores
• Generate composite score and reports

Phase 2: Present Results

Present the generated Markdown report to the user, highlighting:

• Composite score and zone classification
• Exposure guidance (Full/Normal/Reduced/Defensive/Preservation)
• Sector heatmap showing strongest and weakest sectors
• Key momentum and rotation signals

5-Component Scoring System

| # | Component | Weight | Key Signal |

|---|-----------|--------|------------|

| 1 | Market Breadth (Overall) | 30% | Ratio level + trend direction |

| 2 | Sector Participation | 25% | Uptrend sector count + ratio spread |

| 3 | Sector Rotation | 15% | Cyclical vs Defensive balance |

| 4 | Momentum | 20% | Slope direction + acceleration |

| 5 | Historical Context | 10% | Percentile rank in history |

Scoring Zones

| Score | Zone | Exposure Guidance |

|-------|------|-------------------|

| 80-100 | Strong Bull | Full Exposure (100%) |

| 60-79 | Bull | Normal Exposure (80-100%) |

| 40-59 | Neutral | Reduced Exposure (60-80%) |

| 20-39 | Cautious | Defensive (30-60%) |

| 0-19 | Bear | Capital Preservation (0-30%) |

7-Level Zone Detail

Each scoring zone is further divided into sub-zones for finer-grained assessment:

| Score | Zone Detail | Color |

|-------|-------------|-------|

| 80-100 | Strong Bull | Green |

| 70-79 | Bull-Upper | Light Green |

| 60-69 | Bull-Lower | Light Green |

| 40-59 | Neutral | Yellow |

| 30-39 | Cautious-Upper | Orange |

| 20-29 | Cautious-Lower | Orange |

| 0-19 | Bear | Red |

Warning System

Active warnings trigger exposure penalties that tighten guidance even when the composite score is high:

| Warning | Condition | Penalty |

|---------|-----------|---------|

| Late Cycle | Commodity avg > both Cyclical and Defensive | -5 |

| High Spread | Max-min sector ratio spread > 40pp | -3 |

| Divergence | Intra-group std > 8pp, spread > 20pp, or trend dissenters | -3 |

Penalties stack (max -10) + multi-warning discount (+1 when ≥2 active). Applied after composite scoring.

Momentum Smoothing

Slope values are smoothed using EMA(3) (Exponential Moving Average, span=3) before scoring. Acceleration is calculated by comparing the recent 10-point average vs prior 10-point average of smoothed slopes (10v10 window), with fallback to 5v5 when fewer than 20 data points are available.

Historical Confidence Indicator

The Historical Context component includes a confidence assessment based on:

• Sample size: Number of historical data points available
• Regime coverage: Proportion of distinct market regimes (bull/bear/neutral) observed
• Recency: How recent the latest data point is

Confidence levels: High, Medium, Low.

API Requirements

Required: None (uses free GitHub CSV data)

Output Files

• JSON: uptrend_analysis_YYYY-MM-DD_HHMMSS.json
• Markdown: uptrend_analysis_YYYY-MM-DD_HHMMSS.md

Reference Documents

references/uptrend_methodology.md

• Uptrend Ratio definition and thresholds
• 5-component scoring methodology
• Sector classification (Cyclical/Defensive/Commodity)
• Historical calibration notes

When to Load References

• First use: Load uptrend_methodology.md for full framework understanding
• Regular execution: References not needed - script handles scoring